code: def testGetSlotUnderDistributedStrategy(self):
      # Only run this test in graph mode so we don't need actual GPU.
      ds = mirrored_strategy.MirroredStrategy(
          ["CPU:0", "GPU:0"],
          cross_device_ops=cross_device_ops.HierarchicalCopyAllReduce(),
      )
      # We need an optimizer that creates slots.
      optimizer = adam.AdamOptimizer()
  
      def f():
          v = variables.Variable([1.0])
          self.assertTrue(distribute_utils.is_distributed_variable(v))
          # Slot variables are created in the first call to apply_gradients.
          optimizer.apply_gradients([(ops.convert_to_tensor([1.0]), v)])
          self.assertTrue(optimizer.get_slot_names())
          for name in optimizer.get_slot_names():
              slot = optimizer.get_slot(v, name)
              self.assertIsNotNone(slot)
              self.assertTrue(
                  distribute_utils.is_distributed_variable(slot)
              )
  
      ds.run(f)
  
imports: from tensorflow.python.distribute import cross_device_ops
  from tensorflow.python.distribute import distribute_utils
  from tensorflow.python.distribute import mirrored_strategy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import clip_ops
  from tensorflow.python.ops import gradients_util
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.training import adam
  from tensorflow.python.training import gradient_descent
label_desc: Use image node_id 12 for calling the OptimizerTest obj's underlying member method code with example usage: obj.testGetSlotUnderDistributedStrategy() without return types